Antecedents of data analytics adoption: A systematic literature review from 2018-2024

<ns3:p>Background The rise of data analytics adoption has transformed multiple industries through technological advancements. However, utilizing big data analytics presents challenges that depend on adoption models used by individuals or organizations. Whilst numerous models on big data analytics exist, understanding the most influential theories shaping research in this domain remains limited. The study systematically explores the antecedents of data analytics adoption, aims to map the evolution of the field and uncover underexplored domains and integration gaps. Methods A rigorous systematic literature review of 43 peer-reviewed articles published between 2018 and 2024, collected mostly from Scopus and Web of Science databases, was conducted, employing the Preferred Reporting Items for Systematic Reviews and Meta Analysis (PRISMA) guidelines and specific inclusion/exclusion criteria. Advanced bibliometric tools like VOSviewer and Microsoft Excel were employed to identify key trends, thematic clusters and integration gaps. Results The study reveals research concentration in manufacturing sectors and developed Asian countries. The review identifies five interconnected adoption dimensions: technological; organizational; environmental; individual; and data-related factors. The Technology-Organization-Environment (TOE) framework dominates organizational-level studies, while the Unified Theory of Acceptance and Use of Technology (UTAUT) primarily guides individual-level investigations. Having identified five key research clusters, the review highlights that theoretical fragmentation persists between behavioral and resource-based perspectives. Conclusion This study synthesizes the theoretical model of big data analytics research, providing guidance for future researchers in selecting an appropriate theoretical framework, differentiating between individual and organizational adoption levels and identifying significant determinants for technology adoption studies.</ns3:p>